One embodiment provides a method, including: receiving movement data describing physical movement of a person performing a predetermined action; generating, using a processor, classification of the movement data using a test application that predicts output of a wearable device, wherein the test application has been formed using previously collected data that describe the movement of a person performing the predetermined action; determining, using the processor, whether the movement data match the predetermined action in view of the classification; receiving output of a body-aware application that detects and responds to human movement; comparing, using the processor, the output of the body-aware application with the classification; and providing, using the processor, an indication of the comparing of the output of the body-aware application and the classification.

Techniques are disclosed relating to automated operations management. In various embodiments, a computer system accesses operational information that defines commands for an operational scenario and accesses blueprints that describe operational entities in a target computer environment related to the operational scenario. The computer system implements the operational scenario for the target computer environment. The implementing may include executing a hierarchy of controller modules that include an orchestrator controller module at top level of the hierarchy that is executable to carry out the commands by issuing instructions to controller modules at a next level. The controller modules may be executable to manage the operational entities according to the blueprints to complete the operational scenario. In various embodiments, the computer system includes additional features such as an application programming interface (API), a remote routing engine, a workflow engine, a reasoning engine, a security engine, and a testing engine.

Systems and methods are disclosed herein for improving data migration operations including testing and setup of computing environments. In one example, the method may include receiving data for one or more application programming interfaces (APIs). The method may further include generating one or more tests to test the one or more APIs in a first computing environment, testing the APIs, storing the results in a database, and performing a change data capture operation. The method may further include augmenting the one or more tests with the CDC data to generate an updated test. The method may further include testing, using the updated test, a second set of the one or more APIs and comparing the test results. The method may also include outputting a confidence score indicating a correlation between the first environment and the second environment.

Disclosed herein are techniques for analyzing control-flow integrity based on functional line-of-code behavior and relation models. Techniques include receiving data based on runtime operations of a controller; constructing a line-of-code behavior and relation model representing execution of functions on the controller based on the received data; constructing, based on the line-of-code behavioral and relation model, a dynamic control flow integrity model configured for the controller to enforce in real-time; and deploying the dynamic control flow integrity model to the controller.

A system and method for remote testing of enterprise software applications (ESA) allows one or more testers to remotely access an ESA and remotely test the ESA. In at least one embodiment, the ESA resides in a testing platform that includes one more computers that are provisioned for testing. “Provisioning” a computer system (such as one or more servers) refers to preparing, configuring, and equipping the computer system to provide services to one or more users. In at least one embodiment, the computer system is provisioned to create an ESA operational environment in accordance with a virtual desktop infrastructure (VDI) template interacting with virtualization software.

Methods and apparatus are described by which a rich, time-correlated information set is captured during automated testing of an application in a way that allows the application developer to understand the state of the application under test (AUT), the browser interacting with the AUT, and/or the device interacting with the AUT, as it/they changed over time. Mechanisms or features associated with browsers and/or device operating systems are exploited to capture such information, not only for the purpose of better understanding individual test runs, but also to enable the use of analytics over data sets.

A computer-implemented method of evaluating the performance of a full or partial autonomous vehicle (AV) stack in simulation, the method comprising: applying an optimization algorithm to a numerical performance function defined over a scenario space, wherein the numerical performance function quantifies the extent of success or failure of the AV stack as a numerical score, and the optimization algorithm searches the scenario space for a driving scenario in which the extent of failure of the AV stack is substantially maximized, wherein the optimization algorithm evaluates multiple driving scenarios in the search space over multiple iterations, by running a simulation of each driving scenario in a simulator, in order to provide perception inputs to the AV stack, and thereby generate at least one simulated agent trace and a simulated ego trace reflecting autonomous decisions taken in the AV stack in response to the simulated perception inputs, wherein later iterations of the multiple iterations are guided by the results of previous iterations of the multiple iterations, with the objective of finding the driving scenario for which the extent of failure of the AV stack is maximized.

A pseudo language is provided for manipulating the complex variables associated with the orchestration flow. Verbs are specified in the pseudo language. The verbs cause operations to be performed on the complex variables during processing of the orchestration flow. A first verb of the verbs is specified with a first operation of the operations The first operation, when processed, transfers data from a first set of source elements in a source complex variable to new target elements in a target complex variable based on a description of a target schema of the target complex variable. The target complex variable does not include the first subset of source elements and the target schema includes the description of the first subset of the source elements.

A system and method for real-time or near real-time anomaly detection and root cause automation in production environments or in other environments using shrunk dynamic call graphs are provided. The system includes an instrumentation agent that generates shrunk dynamic call graphs and exceptions/errors by injecting monitoring code or probes or call-tags into monitored application, a data agent that forwards collected data to the analysis engine over a network, an analysis engine that performs continuous clustering using machine learning, anomaly, and root cause detection. The system also includes a reporting module to report the anomaly.

The present invention generally relates to the field of automated functional testing, and, more particularly, to a method and system for validation of calculation code against calculation specification. Currently the validation is done manually which is time consuming and effort intensive. Embodiments of present disclosure provide an automated method of validation by generating a schema from the calculation specification, retrieving data based on the schema, executing the intermediate calculations, and comparing the result with the output from the calculation code. The method requires minimal manual input and is a fast, simple, resilient and low-code/no-code technique that can be extended to support any type of calculation code and calculation specifications with minimal or no change.